Table_1_Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient.docx by Yan Wang (15435)

Table_1_Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient.docx by Yan Wang (15435)

Number of highest variance genes selected per omic for each dataset-approach combination. by Teemu J. Rintala (19333003)

DataSheet1_FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm.xlsx by Wenwu Xu (2023315)

DataSheet2_FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm.doc by Wenwu Xu (2023315)

Simulated Design–Build–Test–Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering by Paul van Lent (16876977)

“…Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. …”

Table1_Identification of a ferroptosis-related gene signature predicting recurrence in stage II/III colorectal cancer based on machine learning algorithms.XLSX by Ze Wang (132986)

“…</p><p>Methods: Ferroptosis-related genes were retrieved from the FerrDb and KEGG databases. …”

Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm by Interdis Sci Comp Life Sci (7335308)

“…This work mainly focuses on the following aspects: (1) On the basis of the IPC-MB and DPI, we presented a novel feature selection method called the improved MB discovery algorithm (IMBDA), which can accurately identify direct and indirect regulatory genes when inferring networks. (2) Isolated genes were properly processed by the IDS to optimize the network structure. (3) The performance of IMBDANET was assessed with extensive experiments. …”

Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm by Interdis Sci Comp Life Sci (7335308)

“…<ul><li><a href="https://link.springer.com/article/10.1007/s12539-021-00478-9#auth-Wei-Liu-Aff1-Aff2" target="_blank">Wei Liu</a>, </li><li><a href="https://link.springer.com/article/10.1007/s12539-021-00478-9#auth-Yi-Jiang-Aff1" target="_blank">Yi Jiang</a>, </li><li><a href="https://link.springer.com/article/10.1007/s12539-021-00478-9#auth-Li-Peng-Aff3" target="_blank">Li Peng</a>, </li><li><a href="https://link.springer.com/article/10.1007/s12539-021-00478-9#auth-Xingen-Sun-Aff1" target="_blank">Xingen Sun</a>, </li><li><a href="https://link.springer.com/article/10.1007/s12539-021-00478-9#auth-Wenqing-Gan-Aff1" target="_blank">Wenqing Gan</a>, </li><li><a href="https://link.springer.com/article/10.1007/s12539-021-00478-9#auth-Qi-Zhao-Aff4" target="_blank">Qi Zhao</a> </li><li><a href="https://link.springer.com/article/10.1007/s12539-021-00478-9#auth-Huanrong-Tang-Aff1" target="_blank">Huanrong Tang</a></li></ul><p dir="ltr">A novel network inference method based on the improved MB discovery algorithm, IMBDANET, was proposed for improving gene regulatory networks. …”

The operating principle of APDCA. by Yangsong He (13909371)

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Convergence comparison of APDCA, SIMCLDA, MLFDA, and SDLDA over 100 iterations using relative error as the evaluation metric. by Yangsong He (13909371)

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Comparison of predictive performance under 5-fold cross-validation. by Yangsong He (13909371)

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Network showing the RBP–AS event associations during EMT. by Yangsong He (13909371)

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AUC sensitivity of APDCA to the sparse control parameter <i>k</i> under 5-fold cross-validation. by Yangsong He (13909371)

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AUC values under different combinations of regularization parameters <i>λ</i> and . by Yangsong He (13909371)

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Top 10 QKI–AS Event Associations Predicted by APDCA and Their Evidence in OncoSplicing. by Yangsong He (13909371)

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AUC surface with respect to regularization parameters <i>λ</i> and . by Yangsong He (13909371)

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Schematic illustration of the multi-type biological network used in APDCA. by Yangsong He (13909371)

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DataSheet1_Identification of a ferroptosis-related gene signature predicting recurrence in stage II/III colorectal cancer based on machine learning algorithms.DOCX by Ze Wang (132986)

“…</p><p>Methods: Ferroptosis-related genes were retrieved from the FerrDb and KEGG databases. …”

Table 1_Epilepsy-associated CHD2 missense variants and optimization strategies for genetic diagnosis: a comparative analysis of algorithms.docx by Yu-Jie Gu (20963948)